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Trust-based Multi-Objective Optimization for Node-to-Task Assignment in Coalition Networks. Jin-Hee Cho, Ing-Ray Chen, Yating Wang, and Kevin S. Chan Presented by Jia Guo. Outline. Introduction System Model Task Assignment Protocol Task Assignment Problem
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Trust-based Multi-Objective Optimization for Node-to-Task Assignment in Coalition Networks Jin-Hee Cho, Ing-Ray Chen, Yating Wang, and Kevin S. Chan Presented by Jia Guo
Outline Introduction System Model Task Assignment Protocol Task Assignment Problem Numerical Results And Analysis Conclusions
Introduction Introduction Scenario Objective Contributions
Introduction Scenario Tactical networks deployed to support military missions, disaster management, and/or emergency situations, often require forming a temporary coalition in order to execute a given mission where effective and efficient asset-task assignment is critical to mission success. Under a global objective of completing the mission successfully, the network or system may have multiple objectives to achieve, with participating parties seeking to maximize their own utilities. Multi-objective optimization (MOO) problems
Introduction Objective Identify an optimal solution of multiple task assignments to entities with diverse capabilities/characteristics to meet the multiple objectives. Identifying the optimal set of members for each task team is the key to solving this problem.
Introduction Contribution the first to solve a MOO problem dealing with multiple, concurrent and dynamic coalition formations (task assignments) using a composite trust metric based on multiple trust dimensions. this work proposes and analyzes a new design concept of trust-based MOO by computing risk based on assessed trust levels to screen task team members for node-to-task assignment. the three objectives preserve both individual welfare in terms of maximizing utilization, and global welfare in terms of maximizing mission completion ratio and minimizing extra delay to task completion. perform a comparative analysis of heuristic ranking-based member selection strategy with both a non-trust baseline scheme through simulation study as well as an optimal solution implemented with the Integer Linear Programming (ILP) technique, to demonstrate the effectiveness of their approach.
System Model Node Model Trust Metric Network Model Task Model System Objectives
System Model Node Model M node types, NT1, …, NTM, ordered such that a higher node type has more capability than a lower node type. A node with a higher node type involving human also has more trust dimensions than a node with a lower type node. When not involved in a task, random mobility model. Each node can monitor its neighboring nodes, with detection error specified by false positive and false negative probabilities. Nodes may be malicious,
System Model Trust Metric Social Connectedness (SC) Reciprocity (R) Competence (C) Integrity (I)
System Model the indirect trust which is computed by the average of valid recommendations (trust values) received in that interval. If no fresh evidence is available, the trust component is merely the discounted prior estimate, optimal trust parameter setting to minimize the discrepancy between measured trust and actual trust.
System Model Network Model a mission-oriented tactical network where stationary (i.e., sensors) and/or mobile nodes communicate through multiple hops. a hierarchical structure to execute a mission consisting of multiple tasks. A commander node (CN) governs the mission team. Under the CN, multiple task leaders (TLs) lead task teams. The CN selects TLs at the beginning of network deployment based on the trustworthiness of nodes known to CN a priori and the TLs recruit regular members (RMs) based on periodic dynamic trust assessment. A group key is used for communications among members to prevent outside attackers.
System Model Task Model Required node type (NTm) Required number of nodes (Nm) Minimum trust threshold Importance (Im) Urgency (URm) Difficulty (DFm) A task is regarded as successful if it is completed within its deadline, defined by:
System Model System Objectives (1)
System Model System Objectives (2)
System Model System Objectives (3)
System Model System Objectives
Task Assignment Protocol Advertisement of Task Specification Member Selection Task Failure Trust Reward and Penalty
Task Assignment Protocol Advertisement of Task Specification The task specification disseminated during the auction process includes a set of requirements for task execution specified by:
Task Assignment Protocol An individual node aims to maximize its privilege to access network resources by maintaining its trusted status as a member of the mission team. The payoff considers the busy time for executing a task (i.e., utilization), the task importance and the role of a node (i.e., a TL role gives a higher role score than a RM) .
Task Assignment Protocol Member Selection The inclusion of node j in task m involves a risk, that is linear in urgency but exponential in the trust factor. If sufficient members are not found, TL can re-advertise the task at the next trust update interval when the node’s trust values are updated, assuming that the task can be scheduled to execute to completion before deadline. Otherwise, the task is assumed to have failed.
Task Assignment Protocol Task Failure an appropriate team could not be formed. the team has too many untrustworthy nodes
Task Assignment Protocol Trust Reward and Penalty Based on result of task completion or failure
Task Assignment Problem The node-task optimization problem to maximize PMOO is combinatorial and NP-complete. This means that the optimization problem is not polynomially solvable in runtime. Suppose that all tasks are known a priori – what is the best possible performance? We can formulate this as ILP problem [1] which searches for the best solution that satisfies the constraints and maximizes PMOO for node-to-task assignment.
Task Assignment Problem The ILP will try all possible combinations of wj,m for all j, m’s such that is maximized. The first constraint specifies that for any two concurrent tasks, a node can only be assigned to one of them for execution. However, a node needs not at all be assigned to either task. The second constraint specifies that the required number of qualified nodes must be assigned to task m. The third constraint specifies that a node assigned to task m must be of the required type or higher (more capable).
Task Assignment Problem Two other member selection strategies compared with ILP. Non-trust-based selection: TLs do not use any trust/risk analysis to select members. The TL of task m selects Nm members randomly among all bidders with qualified node type. Ranking-based selection: TLs select members based on trust-based risk analysis discussed in Section IV.B. Top nodes with the lowest risk are selected as members.
Conclusions • We developed a ranking-based member selection scheme which trades off complexity for performance. • The results demonstrate that our scheme has low complexity and yet can achieve performance comparable to that of the optimal solution by ILP and can significantly outperform random selection.
